gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged
gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged (rghosh8, 2026) is a 7 billion parameter chat model. gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged is an open-weights chat model with roughly 7 billion parameters.
by rghosh8 · 7B parameters
Best for
Ways to use gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your rghosh8 API key. osFoundry discovers gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged automatically — assign it to a Maestro role (router, direct, orchestrator, or fallback) in the Pipeline tab and it is live in every chat. Your key, your provider account — no token markup.
Deploy a dedicated endpoint
gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged is open-weights — run it locally for free, or deploy a dedicated GPU endpoint in your workspace for reserved capacity with no rate limits.
Use it in a Room App
Room Apps declare AI features in their manifest, then call them with invokeAI:
import { invokeAI } from '@osfoundry/app-sdk'
// 'summarize' is an AI feature declared in your app manifest.
const result = await invokeAI('summarize', userText)
Call it from your own apps
Once a model is wired into your workspace you can host it as an API and reach it from your own services, scripts, or CI — outside osFoundry.
What hardware can run gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged
gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged runs on a single 16GB consumer GPU (~5 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~17 GB).
gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged
Is gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged free to use?
gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged is free to run locally on your own hardware. Hosted access through osFoundry is metered (input Free (local), output Free (local)). You can switch between local and hosted at any time.
Can I use gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged commercially?
Commercial use is allowed with conditions. Licence terms not specified — verify the upstream model card before commercial use. Check upstream documentation.
How much VRAM does gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged need?
Approximately 5 GB at Q4 quantisation, or 17 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged locally?
Yes. gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged best at?
gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged is well-suited to text generation.
How do I use gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged in osFoundry?
Paste your rghosh8 API key in the key dialog (or deploy the open weights for self-hostable models), assign gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by rghosh8 on April 1, 2026. Source: https://huggingface.co/rghosh8/gsm8k-deepseek-llm-7b-chat-rajat-seed-3407-G-16_merged